|
EDITED BY
Mary Prunicki,
Stanford University, United States
REVIEWED BY
Carl Alexander Frisk,
Norwegian Institute of Bioeconomy Research
(NIBIO), Norway
Jesús Rojo,
Complutense University of Madrid, Spain
*CORRESPONDENCE
Panos G. Georgopoulos
panosg@ccl.rutgers.edu
†
These authors have contributed equally to this
work
TYPE Original Research
PUBLISHED 25 October 2022
DOI 10.3389/falgy.2022.959594
Modeling past and future
spatiotemporal distributions of
airborne allergenic pollen across
the contiguous United States
Xiang Ren1,2†, Ting Cai1,3†, Zhongyuan Mi1,3, Leonard Bielory3,
4
1,2,3,5
Christopher G. Nolte and Panos G. Georgopoulos *
1
Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway,
NJ, United States, 2Department of Chemical and Biochemical Engineering, Rutgers University,
Piscataway, NJ, United States, 3Department of Environmental Sciences, Rutgers University, New
Brunswick, NJ, United States, 4Center for Environmental Measurement and Modeling, U.S.
Environmental Protection Agency, Research Triangle Park, NC, United States, 5Department of
Environmental and Occupational Health and Justice, Rutgers School of Public Health, Piscataway,
NJ, United States
SPECIALTY SECTION
This article was submitted to Environmental &
Occupational Determinants, a section of the
journal Frontiers in Allergy
01 June 2022
30 September 2022
PUBLISHED 25 October 2022
RECEIVED
ACCEPTED
CITATION
Ren X, Cai T, Mi Z, Bielory L, Nolte CG and
Georgopoulos PG (2022) Modeling past and
future spatiotemporal distributions of airborne
allergenic pollen across the contiguous United
States.
Front. Allergy 3:959594.
doi: 10.3389/falgy.2022.959594
COPYRIGHT
© 2022 Ren, Cai, Mi, Bielory, Nolte and
Georgopoulos. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The use,
distribution or reproduction in other forums is
permitted, provided the original author(s) and
the copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic practice.
No use, distribution or reproduction is
permitted which does not comply with these
terms.
Exposures to airborne allergenic pollen have been increasing under the
influence of changing climate. A modeling system incorporating pollen
emissions and atmospheric transport and fate processes has been developed
and applied to simulate spatiotemporal distributions of two major
aeroallergens, oak and ragweed pollens, across the contiguous United States
(CONUS) for both historical (year 2004) and future (year 2047) conditions.
The transport and fate of pollen presented here is simulated using our
adapted version of the Community Multiscale Air Quality (CMAQ) model.
Model performance was evaluated using observed pollen counts at monitor
stations across the CONUS for 2004. Our analysis shows that there is
encouraging consistency between observed seasonal mean concentrations
and corresponding simulated seasonal mean concentrations (oak: Pearson =
0.35, ragweed: Pearson = 0.40), and that the model was able to capture the
statistical patterns of observed pollen concentration distributions in 2004 for
most of the pollen monitoring stations. Simulation of pollen levels for a
future year (2047) considered conditions corresponding to the RCP8.5
scenario. Modeling results show substantial regional variability both in the
magnitude and directionality of changes in pollen metrics. Ragweed pollen
season is estimated to start earlier and last longer for all nine climate regions
of the CONUS, with increasing average pollen concentrations in most
regions. The timing and magnitude of oak pollen season vary across the nine
climate regions, with the largest increases in pollen concentrations expected
in the Northeast region.
KEYWORDS
allergenic pollen, emissions and transport/fate model, climate change, CMAQ
(Community Multiscale Air Quality model), CONUS (contiguous United States)
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distributions and associated potential consequences for public
health. Previous studies of large scale emissions and longrange transport of pollen are summarized in Supplementary
Table S1. Emission models can be constructed based on
physics and phenology, to simulate emissions for the
phenological phases of allergenic plants. The phenological
phases are generally predicted in terms of functions of
selected environmental factors. The transport and fate of
pollen grains after release from the flower can be simulated
using mesoscale meteorological models in conjunction with
atmospheric transport models.
Recent studies modeling pollen emissions, transport and
fate have focused on implementing mechanistic modeling
approaches. A modeling framework incorporating the
Weather Research and Forecast (WRF) model and the
Community Multiscale Air Quality (CMAQ) model was used
to study distributions of multiple allergenic pollens in
southern California (25, 26). Jeon et al. (27) employed the
emission model developed by Efstathiou et al. (28) to simulate
oak pollen emissions in Southeast Texas and used the
emissions in conjunction with the CMAQ model to predict
oak pollen concentrations; however, the model underestimated
oak pollen concentration and was not able to capture its
peaks. Sofiev et al. (29–31) simulated birch pollen emissions
and transport in Europe using the System for Integrated
Modeling of Atmospheric Composition (SILAM). Mechanistic
modeling systems have also been developed and applied to
provide operational forecasts of ragweed pollen concentrations
in Europe (32, 33). It should be noted that the above
mechanistic models have mainly focused on short-term
simulations of pollen concentrations for historical years and/
or for small regions (except for SILAM). Large scale
deterministic emission and transport/fate models need to be
developed and evaluated for investigating the impacts of
climate change on allergenic pollen distributions in the
United States (34).
Our team has developed a platform for large scale airborne
pollen modeling that incorporates our recent semi-mechanistic
model for pollen emissions (16, 28, 35, 36). This new pollen
emission model is coupled in the present study with our
adapted version of the CMAQ model (Supplementary
Figure S1) to simulate spatiotemporal distributions (hourly
with horizontal resolution of 36 km × 36 km) of allergenic oak
and ragweed pollen across the contiguous United States
(CONUS) for both historical (2004) and future (2047)
conditions. We selected oak (spring-flowering) and ragweed
(summer/fall flowering) for analysis because ragweed is a
widely distributed annual weed and its pollen is a leading
cause of hay fever (37) while oak is one of the most allergenic
tree species in the United States (38). The prevalences in
NHANES III for positive test responses to 10 common
allergens were 26.2% for short ragweed and 13.2% for white
oak (39). Simulation results for 2004 were evaluated using
Introduction
Airborne allergenic pollen, emitted from trees, weeds and
grasses, is a major trigger of Allergic Airway Disease (AAD),
affecting 5% to 30% of the population in industrialized
countries (1–5). It has been estimated that pollen-related
asthma emergency department visits across the contiguous
United States (CONUS) will increase by 14% in 2090 under a
high greenhouse gas emission scenario (6). Synergism of
allergenic pollen with air pollutants such as ozone and
particulate matter has been reported and can exacerbate the
AAD of allergy sufferers (7–10). Pollen exposure also
enhances susceptibility to respiratory viral infections,
including SARS-CoV-2 (11–13).
The timing and magnitude of pollen seasons are determined
by various biological and physical processes, where specific
environmental conditions may affect single or multiple
processes simultaneously. Temperature and precipitation have
direct impact on plant phenology (14) and their changes can
lead to shifts in pollen season onset and duration that diverge
for spring-flowering and late-flowering taxa (15).
Meteorological factors also affect the emission and transport
of pollen grains. For instance, higher temperature, wind speed,
and lower humidity favor pollen release (16); accumulated
precipitation reduces airborne pollen via wet deposition (17).
Furthermore, temperature and precipitation exert long-term
effects on species distributions and alter pollen biology at
multiple scales (18). There is evidence that plant populations
are shifting northward where temperatures are cooler (19) as
well as toward areas with greater water availability (20). Due
to the complexities and interdependencies of the above
processes, different observation-based studies, especially those
at continental scales (e.g., the United States, Europe) over
large time spans (10 to 30 years) across multiple taxa (21–23),
have been conducted to assess the impacts of climatic drivers
on pollen timing and levels. For instance, Anderegg et al. (22)
report that pollen seasons started earlier by up to 20 days and
pollen concentrations increased by 21% across North
America, based on pollen observations (all taxa combined)
from 1990 to 2018.
Observation-based methods analyze and associate historical
trends and pollen indices with climatic factors, depending
entirely on the data. However, pollen data are scarce in space
and time - in the United States, only ∼80 pollen sampling
stations (operated by a constellation of agencies and allergy
clinics with different sampling methods) report their data to
the National Allergy Bureau (NAB) of the American Academy
of Allergy, Asthma, and Immunology (AAAAI) (24). In view
of such limitations, process-based modeling of emissions and
transport of allergenic pollens from multiple taxa are needed
to isolate effects of climatic factors on the multiple
underlying processes and to characterize their spatiotemporal
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results for 2004 were evaluated using observed pollen counts
from the 58 monitor stations of AAAAI-NAB (24) operating
across the CONUS in 2004 (Figure 1). Methods for
aeroallergen sampling may differ across monitor stations (46),
with Rotorod samplers, Burkard samplers, and KramerCollins samplers, commonly used (47, 48). AAAAI-NAB
guidelines require the samplers to be situated on an
unobstructed rooftop at least one story above ground and
each station must sample a minimum of 3 days per week with
quality control (47). The meteorological conditions for 2047
correspond to a hypothetical high greenhouse gas emission
scenario (Representative Concentration Pathway RCP8.5) (40).
observed pollen counts from the monitor stations of AAAAINAB operating across the CONUS during that year
(Figure 1). Process analyses were conducted to investigate the
contribution of different physical processes on airborne pollen
concentrations as well as the effects of boundary conditions of
airborne pollen concentration on simulated pollen
concentration patterns and levels.
Materials and methods
Model configuration
The CMAQ-Pollen modeling system was established to
produce hourly concentrations of airborne pollen at 36 km ×
36 km spatial resolution across the CONUS (28, 36). The
configurations of model components are listed in
Supplementary Table S2. The meteorological inputs are
derived from climatological simulations performed using the
Community Earth System Model (CESM), which was
downscaled using the WRF model (40, 41). In the present
study, we performed simulations for a historical year (“2004”)
and for a future year (“2047”); the two years were selected as
representative of the climatological conditions in the
beginning and middle of the 21st century. It should be noted
that the meteorological information in the simulations was
not intended to exactly “match” these actual meteorological
years, since the climatological simulations were performed
without assimilating daily local weather observations (40). The
pollen transport and fate model CMAQ-Pollen was adapted
from version 4.7.1 of the CMAQ modeling system (42, 43).
Pollen grains are treated as inert coarse mode aerosol and
physical properties such as density, diameter and diameter
distributions and other related information (e.g., cutoff
maximum aerosol diameter) of the coarse mode are
incorporated in the relevant CMAQ modules (AERO5), so
that the CMAQ-Pollen model can properly describe pollen
dynamics. Specifically, pollen grains are modeled by adding to
the existing coarse mode and assuming it is fully mixed with
other coarse aerosols (e.g., agricultural and wind-blown dust,
sea spray). The CMAQ modules describe diameter of each
coarse aerosol via a lognormal distribution (44). Due to data
limitation, we assume the means of pollen diameters to be
fixed (oak = 28 μm and ragweed = 18 μm for both historical
and future years) and set relevant parameters (such as pollen
density) the same as those used in our emission model (16).
The CMAQ-Pollen model was run for 2004 and 2047,
covering the CONUS with 36 km × 36 km horizontal grid
spacing, hourly temporal resolution, and 34 layers in the
vertical direction. The vertical spatial resolution (layer height)
varies by grid cell and time, noting that CMAQ uses a sigma
vertical coordinate system (45) based on a pressure at the top
boundary (the model top was set at 50 hPa). The simulation
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Emission model
The pollen emission flux in a cell of the modeling grid with
area of Sg was calculated through Eq. 1:
Fg ¼ Fe Sg Pc
(1)
where Pc is the percentage of area coverage of allergenic plants
in the corresponding modeling grid cell. The upward emission
flux Fe for a unit surface is derived using mass balance of
pollen grain fluxes in the surroundings of allergenic plants
(16); it is formulated using Eq. 2:
Fe ¼
qp Ld Lh (Ke LAI þ Cr Kr (1 þ LAI))
1 þ vd (1 þ LAI)u
(2)
where qp is the annual total emission flux. Ld and Lh are the
daily and hourly flowering likelihoods, respectively. Ke and Kr
(dimensionless) are the lumped meteorology adjustment
factors for direct emission and resuspension fluxes,
respectively. LAI is the leaf area index. Cr is a proportionality
factor relating the resuspension to direct emission flux. u and
vd are the characteristic velocity and the deposition velocity,
respectively. All these terms on the right side of Eq. 2, can
either be measured, or parameterized and approximated
through measurable factors. Details of the derivation and
parameterization of the emission model were presented in Cai
et al. (16). The emission fluxes occur only in the first model
layer, up to 60 meters above ground.
The future-year emissions were calculated using the
meteorological factors for 2047, while the remaining
parameters were the same as those in the historical emission
model. Three main parameters (Ke, Kr, Ld) contribute to
emission differences between historical and future scenarios:
both direct emission and resuspension processes are affected
by temperature, wind speed and humidity [see Eqs 10–14 in
our emission model (16)], and daily flowering process is
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FIGURE 1
Distribution of the 58 studied pollen stations across the nine climate regions in the contiguous United States.
affected by temperature and precipitation [see Table 1 in our
phenology model (36)].
(44). Effects of convection on pollen transport are treated
separately through modules of horizontal and vertical advection.
Physical processes governing transport of
airborne pollen
Initial and boundary conditions
Simulation periods during 2004 and 2047 were selected to
correspond to tree and weed pollen seasons. For oak, the
simulations for the CONUS domain were run from 00:00 of
March 1st through 23:00 of April 30th, 2004 and 2047. For
ragweed, the simulations were run from 00:00 of August 1st
through 23:00 of September 30th. March 1st and August 1st
generally precede the earliest flowering day of oak and
ragweed in the CONUS, respectively; therefore, the
simulations were initialized with no existing pollen.
Boundary Conditions (BC) represent multi-layer
concentration fields for grid cells surrounding the modeling
domain, i.e., the eastern and western boundaries of simulation
domain bordering the Atlantic and Pacific oceans, and
The physical processes governing the transport and fate/
removal of pollen grains from air include cloud processes, dry
deposition, horizontal and vertical advection, and horizontal
and vertical eddy (turbulent) diffusion (Supplementary
Figure S1). The dry deposition process is incorporated in the
vertical diffusion process as a flux boundary condition at the
bottom of the model layer (49) and includes the effect of
gravitational settling, which is the most important process
governing deposition of coarse mode particles. Wet deposition
is incorporated in the cloud processes, which include both incloud and below-cloud scavenging; wet deposition depends on
the precipitation rate and on concentration in cloud water
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TABLE 1 Regional mean and standard deviation of changes between 2047 and 2004 (March-April) in mean and maximum hourly concentration, start
date, season length and exceedance hours for oak pollen, estimated using the CMAQ-Pollen model. (mean ± standard deviation).
Climate region
Central
Mean hourly (%)
Max hourly (%)
Start date (day)
Season length (day)
Exceedance hours (%)
5.0 ± 43.4
−7.8 ± 23.9
−1.6 ± 4.0
−0.4 ± 1.0
2.2 ± 17.3
−32.7 ± 48.4
East North Central
−63.2 ± 24.7
−38.8 ± 73.9
3.1 ± 3.8
−1.5 ± 1.0
Northeast
89.7 ± 117.2
85.0 ± 193.6
−2.2 ± 1.2
−0.6 ± 1.2
31.6 ± 71.7
Northwest
−59.3 ± 39.4
−30.2 ± 54.9
2.9 ± 2.2
−2.1 ± 2.0
−60.7 ± 33.1
3.0 ± 3
18.6 ± 39.6
−1.4 ± 3.4
−0.2 ± 1.2
1.1 ± 24.9
9.4 ± 21.3
South
Southeast
13.2 ± 37.7
5.2 ± 24.6
−7.7 ± 2.5
1.0 ± 1.4
Southwest
5.9 ± 31.3
−0.2 ± 35.7
0.5 ± 3.3
1.4 ± 1.2
7.0 ± 30.1
West
−5.6 ± 31.7
3.0 ± 40.1
4.1 ± 2.8
−0.2 ± 1.1
−0.3 ± 22.9
West North Central
−52.4 ± 41.8
−45.8 ± 32.0
6.8 ± 2.8
−1.4 ± 1.1
−66.6 ± 30.1
1) since observations of pollen counts are generally made near
the ground. The model’s lowest layer on average extends from
0 to 60 m above the ground. Estimates of fractional bias (FB)
of simulated seasonal pollen counts (sum of daily pollen
concentration) are reported as:
northern and southern boundaries adjoining Canada and
Mexico. To investigate the influence of BC on airborne pollen
concentrations, two simulations on the CONUS domain were
run for oak pollen for the period from March 1st to April
30th, 2004 by prescribing BC values for all 34 layers at the
four lateral boundaries as (1) 0 (default) and (2) 10 pollen
grains/m3 at each time step of the CMAQ-Pollen model.
Because BC values are not sensitive to the simulated airborne
pollen concentrations [concentration differences were below
2 pollen grains/m3 for most areas (Supplementary
Figure S6)] and eastern and western boundaries are on the
sea without pollen emissions, we set the BCs of pollen to zero.
FBi ¼ 2
(3)
where FBi is the fractional bias of simulated seasonal pollen
counts at station i, SCSim,i is the simulated seasonal pollen
count at station i, and SCObs,i is the observed seasonal pollen
count at station i. Hit and false rates are common indices for
evaluating the simulated daily pollen concentration.
Procedures reported in the literature were followed to
calculate the hit and false rates at three different
concentration levels (32, 33), i.e. at 10, 50 and 100 pollen
grains/m3, respectively. The details of the calculations are
presented in the Supplementary Material.
Evaluation of model performance
Since, as mentioned in Subsection “Model configuration”,
meteorological inputs were downscaled from a global climate
model without assimilating daily local weather observations
(40), our hourly predictions for pollen concentrations were
not expected to adequately capture fine-scale temporal
variations. Therefore, we calculated “averages” and “upscaled”
hourly predictions to reduce data variability, and evaluated
model performance at seasonal and monthly temporal scales.
It should be noted that such upscaling of the hourly pollen
data can help reduce uncertainties and the biases associated
with using different procedures for aerobiological monitoring
(50). Correlation analysis of the observed seasonal mean
pollen concentrations at individual pollen monitoring stations
(Figure 1) with the corresponding simulated seasonal mean
pollen concentrations was conducted with normalized pollen
data (X 0 ¼ (X X)=s, where X is the sample mean, and s is
the sample standard deviation). The observed pollen
concentrations at each monitor station are paired with the
simulated pollen concentrations in the grid cell that contains
the corresponding pollen monitoring station. The simulated
pollen concentrations are derived from the simulated hourly
concentrations in the model’s lowest (surface) layer (i.e., layer
Frontiers in Allergy
SCSim,i SCObs,i
SCSim,i þ SCObs,i
Process analysis
The Process Analysis preprocessor (PROCAN) was
compiled together with our adapted CMAQ model to activate
the process analysis function (42) in the CMAQ-Pollen
modeling system. The process analysis was conducted to
identify the contributions of each physical process on
estimated airborne pollen concentrations. The physical
phenomena incorporated in the process analysis include cloud
processes, dry deposition, emissions, horizontal and vertical
advection, and horizontal and vertical eddy (turbulent)
diffusion. The process analysis was performed using the time
series of simulated hourly concentrations of allergenic oak
pollen during the pollen season in 2004 in the grid cell that
contains the pollen monitoring station at the Atlanta Allergy
and Asthma Clinic (coordinates: 33.97°N, 84.55°W). This area
has an elevation of 366 m, annual mean temperature of
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their early and late flowering season are plotted in Figure 2.
Oak pollen first appeared in the Southern CONUS in March,
then observed in the Northern CONUS in April. The
simulated maximum mean oak pollen concentration for 2004
is 4,500 pollen grains/m3. Ragweed pollen appeared first in the
Northern CONUS in August and then shifted toward the
Southern CONUS in September. The simulated maximum
mean ragweed pollen was 2 × 104 pollen grains/m3. Figure 3
displays simulated average oak and ragweed pollen
concentrations for different hours of the day. These hourly
concentration profiles (averaged over multiple days) indicate
that our CMAQ-Pollen model can capture important
spatiotemporal variations of airborne allergenic pollen.
Consistent with known diurnal patterns, the oak pollen
concentration in each cell of the modeling grid at 11:00 UTC
is higher than that at 18:00 UTC (averaged over Apr 21–Apr
16.8 °C, and annual mean precipitation of 1,286 mm. We
selected this station (with high annual production and peak
concentration) as an example for process analysis; for other
stations, contribution of each process to pollen prediction can
be different, depending on meteorological conditions that vary
both in space and time.
Results
Spatiotemporal patterns of ambient
pollen concentrations
In order to examine the spatiotemporal distribution
patterns of the simulated airborne pollen, the monthly mean
oak and ragweed pollen concentrations at ground level during
FIGURE 2
Spatial patterns of monthly mean concentrations of (A) oak pollen in March 2004; (B) oak pollen in April 2004; (C) ragweed pollen in August 2004; (D)
ragweed pollen in September 2004, calculated using the CMAQ-Pollen model.
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FIGURE 3
Time slices of spatiotemporal concentration profiles of (A) oak pollen at 11:00 UTC (averaged over Apr 21–Apr 30, 2004); (B) oak pollen at 18:00 UTC
(averaged over Apr 21–Apr 30, 2004); (C) ragweed pollen at 14:00 UTC (averaged over Sept 21–Sept 30, 2004); (D) ragweed pollen at 18:00 UTC
(averaged over Sept 21–Sept 30, 2004), calculated using the CMAQ-Pollen model.
mean concentrations and the corresponding simulated
seasonal mean concentrations for both oak and ragweed
pollens. The Pearson correlation coefficient is 0.345 (p-value
is 0.0252) for oak pollen based on available data from 42
monitoring stations, and 0.399 (p-value is 0.0055) for ragweed
pollen based on data from 47 monitoring stations. The data
points for oak pollen are evenly distributed around the 45degree line. Three ragweed pollen monitoring stations have
larger deviations, compared to other stations, which our
model was not able to capture. The statistical distributions of
the daily simulated pollen concentrations at each pollen
monitoring station during the pollen season, compared with
corresponding observation data are shown in Figure 5. For
each pollen monitoring station, similar distributions of
simulation results and observation data indicate reasonably
good model performance. Our model was able to capture the
distribution patterns of observed pollen concentration for
30, 2004), and the ragweed pollen concentration in each cell of
the modeling grid at 14:00 UTC is higher than that at 18:00
UTC (averaged over Sept 21–Sept 30, 2004).
Evaluation of model performance
CMAQ has been extensively used to study the size, chemical
composition, and atmospheric concentration of particulate
matter, which is allocated into three modes (Aitken,
accumulation, and coarse) in the aerosol modules, based on
its size. Most CMAQ studies have focused on particles of the
Aitken and accumulation modes (51–53). In the present
study, pollen grains are treated as inert coarse mode aerosol
and the performance of CMAQ in predicting pollen grain
numbers is evaluated for the first time. As shown in Figure 4,
there is encouraging consistency between observed seasonal
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FIGURE 4
Scatterplots of normalized observed seasonal mean concentrations and simulated seasonal mean concentrations in 2004 for (A) oak and (B) ragweed
pollen at pollen monitoring stations with available data.
oak pollen level of 10 pollen grains/m3 were between 0% and
30% for most of the studied stations, but the false rate
increased for levels of 50 and 100 pollen grains/m3; the false
rates for ragweed were over 30% for most stations, which
indicates that the CMAQ-Pollen model overestimated the
ragweed pollen concentration. Process analysis results shown
in Supplementary Figure S5 indicate that different processes
can play their roles in determining pollen concentrations,
depending on the meteorological conditions and emissions
that vary diurnally and seasonally.
most of the stations. For oak pollen, the model was also able to
simulate the extreme data points at the stations with high
concentration outliers.
Figure 6 shows the fractional bias of the simulated seasonal
pollen count. The fractional bias for seasonal oak pollen count
was in most cases greater than 0, indicating overestimation of
the pollen concentration. The fractional bias for ragweed
pollen concentration was also greater than 0 for most stations,
again suggesting overestimation of observed levels. The
fractional bias exhibits spatial autocorrelation to some degree:
for instance, we detected spatial autocorrelations of estimated
pollen onset and duration parameters that will affect pollen
prediction (36). However, the model was able to capture the
variation of the pollen observations as shown in the
correlation analysis summarized in Figure 4. Hit and false
rates are metrics for checking whether the simulated and
observed exceedances are consistent and co-located.
Supplementary Figures S3, S4 present the hit rates and false
rates for predicted and observed daily oak and ragweed pollen
concentrations for three pollen levels at the studied stations
during 2004 across the CONUS. The average hit rates for
airborne oak pollen levels of 10, 50 and 100 pollen grains/m3
were 81.9%, 74.1% and 68.6%. The average hit rates for
airborne ragweed pollen levels of 10, 50 and 100 pollen
grains/m3 were 85.7%, 72.1% and 72.0%. This indicates that
the observed exceedances of the three thresholds were mostly
correctly predicted by the present modeling system for pollen
emission and transport/fate. The false rates for an airborne
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Impact of climate change on allergenic
pollen
Our pollen emission and transport/fate model was also used
to study potential climate change impacts on attributes of
allergenic pollen seasons in the CONUS. Supplementary
Figures S8, S9 show the spatial distribution of the mean and
maximum hourly concentrations of oak and ragweed pollen
in 2004 and 2047. The mean and maximum concentrations
vary substantially across the nine climate regions of the
CONUS. For oak pollen, the highest mean and maximum
concentrations occurred in the Central, Southeast and South
regions. For ragweed pollen, the highest mean and maximum
hourly concentrations occurred in the West North Central,
South, and Southwest regions. The simulated mean hourly
concentrations of oak pollen ranged from 1 to 2,442 pollen
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FIGURE 5
Seasonal box plots of normalized daily concentrations (simulated with the CMAQ-Pollen model) of oak pollen (top) and ragweed pollen (bottom)
compared with observed pollen concentrations in 2004 at pollen monitoring stations. Boxes range from the 25th to 75th percentiles with the
dark line denoting median and the dark dots denoting the outliers.
grains/m3 in 2004 and from 1 to 2,360 pollen grains/m3 in 2047;
the simulated maximum hourly concentrations of oak pollen
varied from 12 to 27,306 pollen grains/m3 in 2004 and from 3
to 29,175 pollen grains/m3 in 2047. The decrease in mean
hourly concentration and increase in maximum hourly
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concentration can be explained by the fact that the dispersion
of data will increase with greatest minimum values in many
locations. For instance, significant increases of standard
deviation of hourly oak pollen concentrations were observed
across the State of Idaho, Oregon and Nevada and across
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FIGURE 6
Fractional biases of pollen concentrations calculated with the CMAQ-Pollen model for 2004 in the CONUS. (A) Fractional bias of seasonal oak pollen
counts; (B) Fractional bias of seasonal ragweed pollen counts.
Figures 7, 8 and Supplementary Figure S15 present the
changes of five pollen indices, specifically, mean hourly
concentrations, start date, season length, maximum hourly
concentrations, and exceedance hours for oak pollen from 2004
to 2047. The mean and standard deviation of the changes in
these five oak pollen indices for each climate region are
summarized in Table 1, showing that the impact of climate
change on oak pollen varies substantially across the nine
CONUS climate regions. The mean and maximum hourly
concentrations of oak pollen are predicted to increase in the
Northeast, South and Southeast regions, but to decrease in the
Northwest, East North Central, and West North Central
regions. The Northeast region is estimated to experience on
average the highest increase in mean and maximum hourly
concentrations for oak pollen. The oak pollen season was
estimated to start earlier in the Central, Northeast, South and
Southeast regions. Furthermore, the oak pollen season length
was estimated to shorten by 1–2 days for most regions, except
for the Southeast and Southwest regions. The number of hours
during which the oak pollen concentrations exceed a threshold
value of 13 pollen grains/m3 is estimated to increase the most
in the Northeast region, by 31.6%.
Figures 9, 10 and Supplementary Figure S16 present the
changes in the five pollen indices for ragweed pollen from
2004 to 2047. The regional mean and standard deviation of
the changes in the five ragweed pollen indices are summarized
in Table 2. The response of ragweed pollen to climate change
also varies substantially across the nine climate regions. The
mean and maximum hourly concentrations of ragweed pollen
are predicted to increase significantly in the Northwest,
Southeast, Southwest and West regions. The ragweed pollen
season is estimated to start 1–3 days earlier, with a longer
pollen season in all nine climate regions. The number of
much of the region in Kansas (Supplementary Figure S10), and
these regions had decreasing mean (Figure 7) and increasing
maximum (Supplementary Figure S15A) hourly oak pollen
concentrations. This suggests that the variability of pollen
season can increase across large areas of the CONUS under
changing climate. The simulated mean hourly concentrations
of ragweed pollen ranged from 1 to 11,567 pollen grains/m3
in 2004 and from 1 to 12,187 pollen grains/m3 in 2047; the
simulated maximum hourly concentrations of ragweed pollen
varied from 23 to 3.6 × 105 pollen grains/m3 in 2004 and from
76 to 3.7 × 105 pollen grains/m3 in 2047.
Supplementary Figures S11, S12 present the simulated start
date and season length of oak and ragweed pollen season in 2004
and 2047. The oak pollen season in 2004 and 2047 starts in March
in the Southern US, and in April in the Northern US, while the
ragweed pollen starts from the Northern US in August, and
then shifts toward the Southern US in September. The oak
pollen season length ranged from 12 to 46 days in 2004 and 10
to 46 days in 2047, and the ragweed pollen season length varied
between 30 and 57 days in 2004 and between 32 and 58 days
across the CONUS. Supplementary Figures S13, S14 display
the number of hours during which oak and ragweed pollen
concentration exceeded the threshold values (13 pollen grains/
m3 for oak and 30 pollen grains/m3 for ragweed) in 2004 and
2047 across CONUS. The threshold values were selected based
on clinical symptoms of allergic disease in sensitive patients (5,
54, 55). The oak pollen exceedances ranged from 0 to 1,462 h
in 2004 and from 29 to 1,436 h in 2047, with the highest
numbers appearing in the South, Southeast, Southwest and the
West climate regions. The ragweed pollen exceedances ranged
from 0 to 1,208 h in 2004 and from 29 to 1,234 h in 2047, with
the highest numbers in the West North Central, the Southwest,
West, and the Northwest climate regions.
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FIGURE 7
Changes in mean March-April oak pollen concentrations from beginning (2004) to mid-21st century (2047) estimated for the RCP8.5 climate change
scenario using the CMAQ-Pollen model.
FIGURE 8
Changes in oak pollen season between 2047 and 2004 estimated for the RCP8.5 climate change scenario using the CMAQ-Pollen model.
(A) Changes in oak pollen season start date between 2047 and 2004; (B) Changes in oak pollen season length between 2047 and 2004. Data
were mapped only on cells in which the area coverage of oak trees is greater than zero.
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FIGURE 9
Changes in mean August-September ragweed pollen concentrations from beginning (2004) to mid-21st century (2047) estimated for the RCP8.5
climate change scenario using the CMAQ-Pollen model.
FIGURE 10
Changes in ragweed pollen season between 2047 and 2004 estimated for the RCP8.5 climate change scenario using the CMAQ-Pollen model.
(A) Changes in ragweed pollen season start date between 2047 and 2004; (B) Changes in ragweed pollen season length between 2047 and
2004. Data were mapped only on cells in which the area coverage of ragweed is greater than zero.
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TABLE 2 Regional mean and standard deviation of changes between 2047 and 2004 (August-September) in mean and maximum hourly
concentration, start date, season length and exceedance hours for ragweed pollen, estimated using the CMAQ-Pollen model. (mean ± standard
deviation).
Climate region
Mean hourly (%)
Max hourly (%)
Start date (day)
Season length (day)
Exceedance hours (%)
Central
−2.1 ± 23.3
−1.2 ± 38.1
−2.8 ± 0.7
1.8 ± 0.6
−0.6 ± 28.9
East North Central
−12.5 ± 18.5
2.3 ± 32.3
−2.0 ± 0.8
1.4 ± 0.7
−19.7 ± 9.7
Northeast
−0.6 ± 25.1
0.1 ± 53.5
−1.8 ± 0.7
0.7 ± 0.5
11.9 ± 26.9
Northwest
19.3 ± 40.7
18.6 ± 50.7
−0.7 ± 0.9
0.9 ± 0.8
34.1 ± 195.1
South
0.5 ± 14.3
−5.3 ± 24.1
−3.3 ± 1.0
2.0 ± 0.6
1.8 ± 9.7
Southeast
22.7 ± 21.0
12.4 ± 43.2
−2.9 ± 0.9
1.4 ± 0.6
34.3 ± 36.5
1.2 ± 4.8
Southwest
10.7 ± 14.6
3.7 ± 33.7
−3.1 ± 0.9
2.1 ± 0.7
West
11.4 ± 23.2
9.0 ± 33.6
−1.5 ± 0.8
1.0 ± 0.8
3.9 ± 6.6
West North Central
−2.5 ± 12.0
2.7 ± 25.2
−1.1 ± 1.2
0.8 ± 0.8
−3.8 ± 8.3
and ragweed changes across different climate regions.
Climate change in general will drive higher pollen loads and
lengthen season durations as a consequence of the increasing
temperature and CO2 levels (18). However, these impacts
may be modulated by other, species-specific, factors that
could offset or even reverse the predicted patterns (57). For
instance, some tree species such as birch (58) and oak (59)
could suffer the declining of their populations, as they are
sensitive to increased temperatures and summer droughts.
There have been observation-based studies conducted to
explore taxa-specific effects for monitor locations at
continental scale (21, 23), while mechanistic process-based
models used to analyze future pollen patterns for multiple
taxa are absent (34). A recent study employed a “climateflexible” emission model (34) to project pollen emissions for
13 of the most prevalent airborne pollen taxa over the
United States and compared the climate-driven changes in
pollen timing and magnitude for all taxa (60). It should be
noted that the study used scaling factors (such as the
precipitation factor) to adjust pollen emission flux and
quantified some “averaged” effects for multiple underlying
processes; furthermore, dispersion and transport of pollen in
the atmosphere were not investigated.
Importance of atmospheric transport has been well
recognized for pollen forecasts (61). For instance, 7-yearlong (2005–2011) SILAM model simulations were
performed for ragweed across Europe, highlighting the
important role of long-range transport in forming the high
concentration patterns (32). Our modeling results have
shown that the airborne pollen distribution patterns
(Figure 2) generally follow the emission patterns in
Figure 4 of Cai et al. (16), consistent with the findings in
Kurganskiy et al. (17); furthermore, long-range transport
has significant effects on climate-driven concentration
changes at regional scales. Areas such as Nevada and
northern Texas, without oak coverage (blank areas in
Figure 8) were predicted to have 20%–100% increase in
airborne oak pollen by mid-century; areas such as
hours during which the ragweed pollen concentrations exceed a
threshold value of 30 pollen grains/m3 are estimated to increase
by 1.2%–34.3% in six of the nine climate regions, while there
was a decrease in the Central, East North Central and West
North Central regions. Of course, it should be recognized that
these potential changes correspond specifically to one, high
greenhouse gases emissions scenario (RCP8.5) and should
only be interpreted in that context.
Discussion
Large scale mechanistic pollen forecasting under changing
climatic conditions has been rare; this is one of few efforts
aiming to develop and apply process-based modeling for
species with highly allergenic pollen on a continental scale.
The modeling results indicate that climate change is expected
to increase airborne pollen loads by mid-century across the
CONUS, while more significant increases will occur for areas
where pollen is historically uncommon. For instance, over
40% increase in mean oak pollen concentration will occur
across much of the Northeast and Southwest regions
(Figure 7), where relatively low levels (<200 pollen grains/m3)
were observed at the beginning of the century; over 20%
increase in mean ragweed concentration will occur across
much of the Northeast and Southeast regions (Figure 9),
where relatively low levels (<500 pollen grains/m3) were
observed at the beginning of the century. This finding is
basically consistent with the study of long-term predictions of
ragweed in Europe (56) that considered different plant
invasion scenarios, and reported that sensitization to ragweed
will more than double by mid-century. In the United States, a
recent study has also indicated potential northward expansion
of ragweed due to climate change (37).
Synergistic effects of allergenic pollen are important
metrics for assessing the severity of pollen-related allergen
exposures. Our model can detect both consistent (e.g.,
Northeast) and opposite (e.g., Southeast) patterns for oak
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10.3389/falgy.2022.959594
applications involving multi-year forecasting of long-term
pollen concentrations in the context of climate change.
Massachusetts and Virginia, without ragweed coverage (blank
areas in Figure 10) were predicted to have 20%–80% increase
in airborne ragweed pollen by mid-century. Note that the
same land use and plant coverage were applied in the
present study to simulate historical and future concentration
patterns. For areas with large species coverage, the emission
process plays a dominant role in airborne pollen
concentrations; while contributions of other processes (such
as wind-driven horizontal advection) vary substantially,
depending on meteorological conditions in specific areas
(Supplementary Figure S5). For areas without relevant
species coverage, emissions become zero and the
atmospheric transport processes drive the airborne pollen
concentrations.
The impacts of climate change on airborne allergenic pollen
are complex, relating to multiple processes with large
uncertainties and interdependencies. Apart from the
consequence of increasing temperature and CO2 levels (18),
other climate conditions such as alterations to precipitation
patterns and rise in sea levels may also influence species
distribution and pollen production (14, 62). It should be noted
that the present study is mainly focused on quantifying
important “isolated” effects of climatic factors (including
temperature, precipitation, humidity and wind) on emission and
transport of highly allergenic pollen (oak and ragweed) across
the CONUS. Future changes in land use and other relevant
factors should be further considered and incorporated in
modeling platforms. As an example, Hamaoui-Laguel et al. (63)
developed a comprehensive and integrated process-based
modeling framework that incorporates phenology, plant
population dynamics, pollen production, release and
atmospheric transport to assess future changes in airborne
pollen concentration (daily); the framework was further used for
long-term forecasting and quantitative analyses of climate
change impacts on ragweed pollen allergy in Europe (56).
Previous studies modeled impacts of climate change
generally based on long-term predictions of pollen
concentrations at continental or global scales (56, 60).
Extrapolation for both past and future behaviors can be
improved by calculating the average of simulations for
multiple years; this is particularly true for empirical and
simplified prognostic models (60). By comparison, processbased models are able to isolate effects of specific factors and
are more robust and interpretable (Supplementary
Figure S5), due to the fact that mechanistic patterns extracted
from underlying processes can be easily extended to both past
and future conditions. It should be recognized that the
present study produced process-based modeling outcomes for
one historic (2004) and one future (2047) year in order to
characterize plausible changes in pollen concentrations from
beginning to mid-century under a specific (RCP8.5) scenario,
without accounting for interannual variability. Extensive work
is ongoing to extend the CMAQ-Pollen model for
Frontiers in Allergy
Conclusions
A CONUS-wide CMAQ-Pollen modeling system
incorporating pollen emissions and transport has been
developed to simulate spatiotemporal distributions of allergenic
oak and ragweed pollens. Model performance was evaluated
through correlation analysis, statistical distributions, hit and
false rates, and fractional bias using observed pollen counts at
monitor stations across the contiguous United States (CONUS)
in 2004. The modeling results revealed encouraging consistency
with observed pollen concentrations and the model captured
important diurnal/seasonal patterns and spatial variations for
both oak and ragweed pollens. Five pollen indices (mean
hourly concentrations, maximum hourly concentrations, start
date, season length, and exceedance hours) were calculated and
compared for each pollen species across the nine climate
regions of the CONUS for 2004 and for 2047. It was estimated
that ragweed pollen season will start earlier and last longer
under the RCP8.5 scenario for all the nine climate regions,
with increasing average pollen concentrations in most regions.
The response of the oak pollen season to climate change varies
across the nine climate regions, with the largest increase in
pollen concentration predicted for the Northeast region. We
found that long-range transport had significant effects on
climate-driven concentration changes, particularly for areas
where pollen was historically uncommon. These effects were
influenced by multiple underlying processes that can be
isolated and interpreted via process analysis and integration.
Additional quantitative studies incorporating modeling efforts
that will consider alternative scenarios regarding future patterns
of vegetation in long-term forecasting of airborne allergenic
pollen, are needed to expand and to improve the
understanding of climate change impacts on allergic disease.
Data availability statement
Model parameters and outputs are available upon request.
Requests to access these datasets should be directed to
panosg@ccl.rutgers.edu.
Author contributions
All authors contributed to the article and approved the
submitted version.
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Ren et al.
10.3389/falgy.2022.959594
Funding
Conflict of interest
This research was funded by the Center for Environmental
Exposures and Disease at EOHSI (NIEHS Grant P30ES005022)
and by the NJ Alliance for Clinical Translational Science (NIH
Grant UL1TROO3017). Additional support was provided by the
U.S. Environmental Protection Agency (EPA) under STAR
Grant EPA-RD-83454701-0 to Rutgers University, and by the
Ozone Research Center which is funded by the State of New
Jersey Department of Environmental Protection (Grant AQ05011). The views expressed in this article are those of the
authors and do not necessarily represent the views or the
policies of the funding agencies.
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their
affiliated organizations, or those of the funding agencies, the
publisher, the editors and the reviewers. Any product that
may be evaluated in this article, or claim that may be made
by its manufacturer, is not guaranteed or endorsed by the
publisher.
Acknowledgments
We thank NAB for providing airborne pollen data, and Ms.
Linda Everett and Mr. George M. Grindlinger (Rutgers) for
editorial and technical assistance. The views expressed in this
article are those of the authors and do not necessarily
represent the views or the policies of the U.S. Environmental
Protection Agency.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/falgy.
2022.959594/full#supplementary-material.
References
1. Bielory L, Lyons K, Goldberg R. Climate change and allergic disease. Curr
Allergy Asthma Rep. (2012) 12(6):485–94. doi: 10.1007/s11882-012-0314-z
rates, as evidenced from 31 countries across the globe. Proc Natl Acad Sci USA.
(2021) 118(12):e2019034118. doi: 10.1073/pnas.2019034118
2. Breton M-C, Garneau M, Fortier I, Guay F, Louis J. Relationship between
climate, pollen concentrations of Ambrosia and medical consultations for
allergic rhinitis in Montreal, 1994–2002. Sci Total Environ. (2006) 370(1):39–50.
doi: 10.1016/j.scitotenv.2006.05.022
12. Dbouk T, Drikakis D. On pollen and airborne virus transmission. Phys
Fluids. (2021) 33(6):063313. doi: 10.1063/5.0055845
13. Romanello M, McGushin A, Di Napoli C, Drummond P, Hughes N, Jamart
L, et al. The 2021 report of the lancet countdown on health and climate change:
code red for a healthy future. Lancet. (2021) 398(10311):1619–62. doi: 10.1016/
S0140-6736(21)01787-6
3. Cakmak S, Dales RE, Burnett RT, Judek S, Coates F, Brook JR. Effect of
airborne allergens on emergency visits by children for conjunctivitis and
rhinitis. Lancet. (2002) 359(9310):947–8. doi: 10.1016/S0140-6736(02)08045-5
14. Schramm P, Brown C, Saha S, Conlon K, Manangan A, Bell J, et al. A
systematic review of the effects of temperature and precipitation on
pollen concentrations and season timing, and implications for human
health. Int J Biometeorol. (2021) 65(10):1615–28. doi: 10.1007/s00484-02102128-7
4. D’Amato G, Chong-Neto HJ, Monge Ortega OP, Vitale C, Ansotegui I, Rosario
N, et al. The effects of climate change on respiratory allergy and asthma induced by
pollen and mold allergens. Allergy. (2020) 75(9):2219–28. doi: 10.1111/all.14476
5. Sofiev M, Bergmann K-C. Allergenic pollen: A review of the production,
release, distribution and health impacts. Dordrecht: Springer (2013). ISBN:
9400748817.
15. Pearson KD. Spring-and fall-flowering species show diverging phenological
responses to climate in the Southeast USA. Int J Biometeorol. (2019) 63(4):481–92.
doi: 10.1007/s00484-019-01679-0
6. Neumann JE, Anenberg SC, Weinberger KR, Amend M, Gulati S, Crimmins
A, et al. Estimates of present and future asthma emergency department visits
associated with exposure to oak, birch, and grass pollen in the United States.
GeoHealth. (2019) 3(1):11–27. doi: 10.1029/2018GH000153
16. Cai T, Zhang Y, Ren X, Bielory L, Mi Z, Nolte CG, et al. Development of a
semi-mechanistic allergenic pollen emission model. Sci Total Environ. (2019)
653:947–57. doi: 10.1016/j.scitotenv.2018.10.243
7. Adhikari A, Reponen T, Grinshpun SA, Martuzevicius D, LeMasters G.
Correlation of ambient inhalable bioaerosols with particulate matter and ozone: a
two-year study. Environ Pollut. (2006) 140(1):16–28. doi: 10.1016/j.envpol.2005.07.004
17. Kurganskiy A, Skjøth CA, Baklanov A, Sofiev M, Saarto A, Severova E,
et al. Incorporation of pollen data in source maps is vital for pollen dispersion
models. Atmos Chem Phys. (2020) 20(4):2099–121. doi: 10.5194/acp-20-20992020
8. Cakmak S, Dales RE, Coates F. Does air pollution increase the effect of
aeroallergens on hospitalization for asthma? J Allergy Clin Immunol. (2012) 129
(1):228–31. doi: 10.1016/j.jaci.2011.09.025
18. Ziska LH. Climate, carbon dioxide, and plant-based aero-allergens: a deeper
botanical perspective. Front Allergy. (2021) 2:714724. doi: 10.3389/falgy.2021.
714724
9. Dales RE, Cakmak S, Judek S, Dann T, Coates F, Brook JR, et al. Influence of
outdoor aeroallergens on hospitalization for asthma in Canada. J Allergy Clin
Immunol. (2004) 113(2):303–6. doi: 10.1016/j.jaci.2003.11.016
19. Chen I-C, Hill JK, Ohlemüller R, Roy DB, Thomas CD. Rapid range shifts of
species associated with high levels of climate warming. Science. (2011) 333
(6045):1024–6. doi: 10.1126/science.1206432
10. Kim KH, Jahan SA, Kabir E. A review on human health perspective of air
pollution with respect to allergies and asthma. Environ Int. (2013) 59:41–52.
doi: 10.1016/j.envint.2013.05.007
20. Harsch MA, HilleRisLambers J. Climate warming and seasonal
precipitation change interact to limit species distribution shifts across
western North America. PloS One. (2016) 11(7):e0159184. doi: 10.1371/
journal.pone.0159184
11. Damialis A, Gilles S, Sofiev M, Sofieva V, Kolek F, Bayr D, et al. Higher
airborne pollen concentrations correlated with increased SARS-CoV-2 infection
Frontiers in Allergy
15
frontiersin.org
Ren et al.
10.3389/falgy.2022.959594
21. Ziello C, Sparks TH, Estrella N, Belmonte J, Bergmann KC, Bucher E, et al.
Changes to airborne pollen counts across Europe. PloS One. (2012) 7(4):e34076.
doi: 10.1371/journal.pone.0034076
CMIP5 archive using the WRF model. J Climate. (2016) 29(2):839–53. doi: 10.
1175/JCLI-D-15-0233.1
22. Anderegg WR, Abatzoglou JT, Anderegg LD, Bielory L, Kinney PL, Ziska L.
Anthropogenic climate change is worsening North American pollen seasons. Proc
Natl Acad Sci USA. (2021) 118(7):e2013284118. doi: 10.1073/pnas.2013284118
42. Byun D, Schere KL. Review of the governing equations, computational
algorithms, and other components of the models-3 community multiscale air
quality (CMAQ) modeling system. Appl Mech Rev. (2006) 59(2):51–77. doi: 10.
1115/1.2128636
23. Zhang Y, Bielory L, Mi Z, Cai T, Robock A, Georgopoulos P. Allergenic
pollen season variations in the past two decades under changing climate in the
United States. Glob Chang Biol. (2015) 21(4):1581–9. doi: 10.1111/gcb.12755
43. Foley K, Roselle S, Appel K, Bhave P, Pleim J, Otte T, et al. Incremental
testing of the community multiscale air quality (CMAQ) modeling system
version 4.7. Geosci Model Dev. (2010) 3(1):205–26. doi: 10.5194/gmd-3-205-2010
24. Schmidt CW. Pollen overload: seasonal allergies in a changing climate.
Environ Health Perspect. (2016) 124:71–5. doi: 10.1289/ehp.124-A70
44. Binkowski FS, Roselle SJ. Models-3 community multiscale air quality
(CMAQ) model aerosol component 1. Model Description. J Geophys Res Atmos.
(2003) 108(D6):1–18. doi: 10.1029/2001JD001409
25. Duhl T, Zhang R, Guenther A, Chung S, Salam M, House J, et al. The
simulator of the timing and magnitude of pollen season (STaMPS) model: a
pollen production model for regional emission and transport modeling. Geosci
Model Dev Discuss. (2013) 6(2):2325–68. doi: 10.5194/gmdd-6-2325-2013
45. USEPA. Operational Guidance for the Community Multiscale Air Quality
(CMAQ) Modeling System (Version 4.7.1). (2010).
46. Oteros J, Galán C, Alcázar P, Domínguez-Vilches E. Quality control in biomonitoring networks, spanish aerobiology network. Sci Total Environ. (2013)
443:559–65. doi: 10.1016/j.scitotenv.2012.11.040
26. Zhang R, Duhl T, Salam M, House J, Flagan R, Avol E, et al. Development of
a regional-scale pollen emission and transport modeling framework for
investigating the impact of climate change on allergic airway disease.
Biogeosciences. (2014) 11(6):1461–78. doi: 10.5194/bg-11-1461-2014
47. Portnoy J, Barnes C, Barnes CS. The national allergy bureau: pollen and
spore reporting today. J Allergy Clin Immunol. (2004) 114(5):1235–8. doi: 10.
1016/j.jaci.2004.07.062
27. Jeon W, Choi Y, Roy A, Pan S, Price D, Hwang M-K, et al. Investigation of
primary factors affecting the variation of modeled oak pollen concentrations: a
case study for southeast Texas in 2010. Asia Pac J Atmos Sci. (2018) 54
(1):33–41. doi: 10.1007/s13143-017-0057-9
48. Vitalpur G, Padgett S, Kloepfer KM, Slaven J, Leickly FE. Variations in
pollen counts between Indianapolis, IN, and dayton, OH, in spring 2013 and
2014. Ann Allergy Asthma Immunol. (2016) 117(3):328–9. doi: 10.1016/j.anai.
2016.06.028
28. Efstathiou C, Isukapalli S, Georgopoulos P. A mechanistic modeling system
for estimating large scale emissions and transport of pollen and co-allergens.
Atmos Environ. (2011) 45(13):2260–76. doi: 10.1016/j.atmosenv.2010.12.008
49. Pleim J, Ran L. Surface flux modeling for air quality applications.
Atmosphere (Basel). (2011) 2(3):271–302. doi: 10.3390/atmos2030271
29. Siljamo P, Sofiev M, Filatova E, Grewling L, Jager S, Khoreva E, et al. A
numerical model of birch pollen emission and dispersion in the atmosphere.
Model evaluation and sensitivity analysis. Int J Biometeorol. (2013) 57
(1):125–36. doi: 10.1007/s00484-012-0539-5
50. Adamov S, Lemonis N, Clot B, Crouzy B, Gehrig R, Graber M-J, et al. On
the measurement uncertainty of hirst-type volumetric pollen and spore samplers.
Aerobiologia. (2021):1–15. doi: 10.1007/s10453-021-09724-5
51. Gantt B, Kelly J, Bash J. Updating sea spray aerosol emissions in the
community multiscale air quality (CMAQ) model version 5.0.2. Geosci Model
Dev. (2015) 8(11):3733–46. doi: 10.5194/gmd-8-3733-2015
30. Sofiev M, Berger U, Prank M, Vira J, Arteta J, Belmonte J, et al. MACC
Regional multi-model ensemble simulations of birch pollen dispersion in
Europe. Atmos Chem Phys. (2015) 15:8115–30. doi: 10.5194/acp-15-8115-2015
52. Nolte C, Appel K, Kelly J, Bhave P, Fahey K, Collett Jr J, et al. Evaluation of
the community multiscale air quality (CMAQ) model v5.0 against size-resolved
measurements of inorganic particle composition across sites in North America.
Geosci Model Dev. (2015) 8(9):2877–92. doi: 10.5194/gmd-8-2877-2015
31. Sofiev M, Siljamo P, Ranta H, Linkosalo T, Jaeger S, Rasmussen A, et al. A
numerical model of birch pollen emission and dispersion in the atmosphere.
Description of the emission module. Int J Biometeorol. (2013) 57(1):45–58.
doi: 10.1007/s00484-012-0532-z
53. Wang Z, Itahashi S, Uno I, Pan X, Osada K, Yamamoto S, et al. Modeling
the long-range transport of particulate matters for January in east Asia using
NAQPMS and CMAQ. Aerosol Air Qual Res. (2017) 17(12):3065–78. doi: 10.
4209/aaqr.2016.12.0534
32. Prank M, Chapman DS, Bullock JM, Belmonte J, Berger U, Dahl A, et al. An
operational model for forecasting ragweed pollen release and dispersion in
Europe. Agric for Meteorol. (2013) 182–183:43–53. doi: 10.1016/j.agrformet.
2013.08.003
54. Rapiejko P, Stanlaewicz W, Szczygielski K, Jurkiewicz D. Threshold pollen
count necessary to evoke allergic symptoms. Otolaryngol Pol. (2007) 61
(4):591–4. doi: 10.1016/S0030-6657(07)70491-2
33. Zink K, Vogel H, Vogel B, Magyar D, Kottmeier C. Modeling the dispersion
of Ambrosia artemisiifolia L. Pollen with the model system COSMO-ART. Int
J Biometeorol. (2012) 56(4):669–80. doi: 10.1007/s00484-011-0468-8
55. Thibaudon M. The pollen-associated allergic risk in France. Eur Ann Allergy
Clin Immunol. (2003) 35(5):170–2.
34. Wozniak MC, Steiner AL. A prognostic pollen emissions model for climate
models (PECM1.0). Geosci Model Dev. (2017) 10(11):4105–27. doi: 10.5194/gmd10-4105-2017
56. Lake IR, Jones NR, Agnew M, Goodess CM, Giorgi F, Hamaoui-Laguel L,
et al. Climate change and future pollen allergy in Europe. Environ Health
Perspect. (2017) 125(3):385–91. doi: 10.1289/EHP173
35. Cai T. Modeling impacts of climate change on air quality and associated
human exposures. New Brunswick: Rutgers The State University of New Jersey,
School of Graduate Studies (2019).
57. Fei S, Desprez JM, Potter KM, Jo I, Knott JA, Oswalt CM. Divergence of
species responses to climate change. Sci Adv. (2017) 3(5):e1603055. doi: 10.
1126/sciadv.1603055
36. Zhang Y, Bielory L, Cai T, Mi Z, Georgopoulos P. Predicting onset and
duration of airborne allergenic pollen season in the United States. Atmos
Environ. (2015) 103:297–306. doi: 10.1016/j.atmosenv.2014.12.019
58. Rojo J, Oteros J, Picornell A, Maya-Manzano JM, Damialis A, Zink K, et al.
Effects of future climate change on birch abundance and their pollen load. Glob
Chang Biol. (2021) 27(22):5934–49. doi: 10.1111/gcb.15824
37. Case MJ, Stinson KA. Climate change impacts on the distribution of the
allergenic plant, common ragweed (Ambrosia artemisiifolia) in the eastern
United States. PloS One. (2018) 13(10):e0205677. doi: 10.1371/journal.pone.
0205677
59. Di Filippo A, Alessandrini A, Biondi F, Blasi S, Portoghesi L, Piovesan G.
Climate change and oak growth decline: dendroecology and stand productivity
of a Turkey oak (Quercus cerris L.) old stored coppice in central Italy. Ann for
Sci. (2010) 67(7):706. doi: 10.1051/forest/2010031
38. Pasken R, Pietrowicz JA. Using dispersion and mesoscale meteorological
models to forecast pollen concentrations. Atmos Environ. (2005) 39
(40):7689–701. doi: 10.1016/j.atmosenv.2005.04.043
60. Zhang Y, Steiner AL. Projected climate-driven changes in pollen emission
season length and magnitude over the continental United States. Nat Commun.
(2022) 13(1):1–10. doi: 10.1038/s41467-021-27699-2
39. Arbes Jr SJ, Gergen PJ, Elliott L, Zeldin DC. Prevalences of positive skin test
responses to 10 common allergens in the US population: results from the third
national health and nutrition examination survey. J Allergy Clin Immunol.
(2005) 116(2):377–83. doi: 10.1016/j.jaci.2005.05.017
61. Sofiev M. On impact of transport conditions on variability of the seasonal
pollen index. Aerobiologia. (2017) 33(1):167–79. doi: 10.1007/s10453-016-9459-x
40. Nolte CG, Spero TL, Bowden JH, Mallard MS, Dolwick PD. The potential
effects of climate change on air quality across the conterminous US at 2030
under three representative concentration pathways. Atmos Chem Phys. (2018)
18(20):15471–89. doi: 10.5194/acp-18-15471-2018
62. Bilskie M, Hagen S, Medeiros S, Passeri D. Dynamics of sea level rise and
coastal flooding on a changing landscape. Geophys Res Lett. (2014) 41
(3):927–34. doi: 10.1002/2013GL058759
63. Hamaoui-Laguel L, Vautard R, Liu L, Solmon F, Viovy N, Khvorostyanov D,
et al. Effects of climate change and seed dispersal on airborne ragweed pollen
loads in Europe. Nat Clim Change. (2015) 5(8):766–71. doi: 10.1038/nclimate2652
41. Spero TL, Nolte CG, Bowden JH, Mallard MS, Herwehe JA. The impact of
incongruous lake temperatures on regional climate extremes downscaled from the
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